Elrod Julia, Mohr Christoph, Wolff Ruben, Boettcher Michael, Reinshagen Konrad, Bartels Pia, Koenigs Ingo
Department of Paediatric Surgery, University Medical Centre Eppendorf, Hamburg, Germany.
Burn Unit, Plastic and Reconstructive Surgery, Department of Paediatric Surgery, Altona Children's Hospital, Hamburg, Germany.
Front Pediatr. 2021 Jan 18;8:613736. doi: 10.3389/fped.2020.613736. eCollection 2020.
It is not only important for counseling purposes and for healthcare management. This study investigates the prediction accuracy of an artificial intelligence (AI)-based approach and a linear model. The heuristic expecting 1 day of stay per percentage of total body surface area (TBSA) serves as the performance benchmark. The study is based on pediatric burn patient's data sets from an international burn registry ( = 8,542). Mean absolute error and standard error are calculated for each prediction model (rule of thumb, linear regression, and random forest). Factors contributing to a prolonged stay and the relationship between TBSA and the residual error are analyzed. The random forest-based approach and the linear model are statistically superior to the rule of thumb ( < 0.001, resp. = 0.009). The residual error rises as TBSA increases for all methods. Factors associated with a prolonged LOS are particularly TBSA, depth of burn, and inhalation trauma. Applying AI-based algorithms to data from large international registries constitutes a promising tool for the purpose of prediction in medicine in the future; however, certain prerequisites concerning the underlying data sets and certain shortcomings must be considered.
这不仅对咨询目的和医疗管理很重要。本研究调查了基于人工智能（AI）的方法和线性模型的预测准确性。将每百分比体表面积（TBSA）对应1天住院时间的经验法则作为性能基准。该研究基于来自国际烧伤登记处的儿科烧伤患者数据集（n = 8542）。为每个预测模型（经验法则、线性回归和随机森林）计算平均绝对误差和标准误差。分析了导致住院时间延长的因素以及TBSA与残差之间的关系。基于随机森林的方法和线性模型在统计学上优于经验法则（分别为p < 0.001和p = 0.009）。对于所有方法，残差随着TBSA的增加而上升。与住院时间延长相关的因素特别是TBSA、烧伤深度和吸入性创伤。将基于AI的算法应用于来自大型国际登记处的数据，构成了未来医学预测目的的一种有前景的工具；然而，必须考虑有关基础数据集的某些先决条件和某些缺点。
